Paralysis from spinal cord injuries, degenerative diseases and birth defects can reduce the autonomy and quality of life for those so afflicted. Existing assistive devices for people with severe motor disabilities are inherently limited, relying primarily on residual motor function for their use. For example, the Sip-and-Puff system can control a motorized wheelchair using air pressure by “sipping” (inhaling) or “puffing” (exhaling) into a straw with a pressure sensor.
More recently, brain-computer interfaces (BCIs) are being used to control assistive devices such as computer cursors or robotic arms by decoding neural activity directly from the brain. However, nonstationarities in recorded brain signals can degrade the quality of neural decoding over time. Furthermore, a periodic and frequent interruption to recalibrate the neural decoding algorithm would be both time-consuming and impractical. Signal nonstationarities and very low signal to noise ratios in BCI sensors have thus far limited the ability of disabled persons to have autonomous control of assistive devices, thus significantly diminishing their quality of life.